Mapping and navigation in robotics is a very widely researched problem. Within the field, building topological maps has seen special interest due to various reasons~\cite{Thrun199821}. 
One of the most important advantages of topological maps for an intelligent agent is its low space complexity~\cite{Thrun199821}. 
While in a metric map a robot can be in infinitely many different positions, in topological representation this is typically reduced to being in one of the vertices, greatly simplifying the complexity of the subsequent computations.
This reduction in complexity has a side effect in the loss of precision, however previous research have shown that high spatial accuracy is not crucial for a wide variety of mobility tasks~\cite{Kuipers04LocalMapsInSSH}.

Various researchers have studied the problem of segmenting metric space to build topological maps~\cite{Kuipers1983a,Ranganathan2006}. These works aimed at detecting natural separators such as doorways to add a new vertex in the topology~\cite{Thrun96h,Anguelov2004}. Another example is by~\cite{Shatkay1997} where the authors made use of odometric data to improve the mapping accuracy.

More recently, researchers became interested in augmenting topological maps with semantic information about the contents of the mapped environment. A nice example of this is~\cite{Mozos2007}, where by boosting several weak classifiers a mapping between sensory data to room categories is learned. 
\begin{figure}[hbpt]
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  \includegraphics[scale=0.5]{figures/examplegraph}
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  \caption{An example graph from the dataset.}
  \label{fig:sim}
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\subsection{Contributions}
Imagine a mobile robot tasked with finding an object on an unexplored building floor. The robot needs to plan its actions to complete the task of object search and the plan quality depends on the accuracy of the robot's expectations. As an example, having found a corridor and an office, its expectation of finding another room by exploring the corridor should be higher than exploring the office as corridors act as connectors in most indoor environments. 
In most systems where structural information can be used, this relation between the connectivity of room types is hard-coded. Instead, we leverage on the MIT floorplan database~\cite{Whiting2007topology}, containing 567 floors, 6426 spaces with 91 space categories and 8446 connections between the spaces in total. First, we provide an analysis of the topological properties of a large indoor floorplan dataset. To the best of our knowledge, no previous work exists on providing insights in indoor topologies of this scale. Second, we develop a system to predict both the structure (i.e. which type of rooms are connected to each other) and the node labelings (i.e. which type of rooms are most commonly found) from a large real-world annotated semantic indoor topology database.  
The hypothesis is that indoor environments are topologically arranged in small functional units, e.g. $\{corridor-bathroom-office\}$ or $\{corridor-mailbox-office\}$. 
Therefore by extracting these frequently occuring topological patterns we can make accurate predictions even though the specific input graph at hand contains rooms of previously unknown categories. 
Rooms with unknown categories in the input graph corresponds to a real world problem where a robot's classifier may be largely uncertain about a room's category or that the robot has no model for that specific room. Even in this case, the system should still provide reasonable predictions. 
%\missingfigure{Figure of an example graph(s). This should be at the top of 2nd column in 1st page}


